Agricultural sprayers are used to deliver fluid treatments that protect and improve crop plant health. “Drift” occurs when small droplets of these fluids are driven by the wind beyond their proper placement. Drift can result in crops within a target area receiving too little or too much treatment, and can result in undesirable effects on non-target organisms and on air and water quality outside of the target area. The U.S. Environmental Protection Agency has promulgated regulations for controlling drift into sensitive areas.
Several mathematical models have been used to determine and adjust for the propagation of drift, including Lagrangian, Gaussian diffusion, plume, regression, random walk, and computational fluid dynamic models. These models have varying degrees of accuracy based on differing assumptions and differing abilities to measure or estimate relevant parameters. For example, regression models exhibit poor performance when current conditions are substantially different from the conditions on which the models were built, and attempts to incorporate random fluctuations have not sufficiently enhanced performance; plume models exhibit poor performance at short distances; and computational fluid dynamic models are computationally intensive unless simplified at the expense of increased errors.
One solution for addressing drift has been to attach wind sensors to the ends of sprayer booms to detect current wind direction and speed and then use one of these models to control nozzle parameters to alter droplet size to reactively control drift. However, these models all rely on current wind conditions and are therefore slow to adapt to rapid changes in wind direction and speed. In particular, if the wind changes speed or direction soon after a droplet has left the nozzle, then undesirable drift can still occur with these prior art solutions.
This background discussion is intended to provide information related to the present invention which is not necessarily prior art.